function [vL,vR]=RFNN_EKF_cal_v(dg,do,sig,sio,v_o,w_o)

    coder.inline("never");

    %输入向量
    aph=0.3;
    b=0.098;
    S=[dg,do,sig,sio];
    % 初始化静态变量
    persistent a;
    if isempty(a)
%             a=[-0.027,0.19,-0.079,0.12,0.077,0.087,0.009,0.136,0.077,-0.017,0.17,-0.011,0.03,0.095,0.07,-0.014;
%                 -0.061,0.06,0.065,0.164,0.052,0.007,0.001,0.208,0.216,0.016,0.2,-0.021,0.159,-0.006,0.125,-0.039];
%             a=[0.16,0.205,0.193,0.15,0.081,0.109,0.046,0.122,0.063,0.023,0.22,0.132,0.13,0.081,0.11,0.121;
%                 0.266,0.09,0.289,0.222,0.115,0.059,0.103,0.185,0.195,0.046,0.172,0.069,0.234,0.169,0.23,0.297];
            a=[0.1057,0.142,0.1664,0.6209,0.5737,0.052,0.9312,0.7286,0.7378,0.0634,0.8604,0.9344,0.9843,0.8589,0.7855,0.5133;
                0.1776,0.3985,0.1339,0.0308,0.9391,0.3013,0.2955,0.3329,0.467,0.6481,0.0252,0.8422,0.559,0.854,0.3478,0.446];%VR上VL下
    end
    %step end
    %
    %神经网络部分计算输出
    W=a*sum(S(:));
    [Outputs,Fnk]=RFNNpro_v(S,W);

    vL=Outputs(2)/(1+aph*abs(Outputs(2)));
    vR=Outputs(1)/(1+aph*abs(Outputs(1)));
%     V=(vL+vR)/2;
%     vL=Outputs(1);
%     vR=Outputs(2);
    %EKF修正
    if(do>2)
        S1=[dg,sig];
    else
        S1=S;
    end
    a_32=[a(1,:),a(2,:)];
    a1=EKF_weight_v3(S1,Fnk,Outputs,a_32,v_o,w_o,aph,b);
    a=[a1(1:16);a1(17:32)];
end